Description
What happened?
The lazy computation time seems to be dependent on the indexers size in Dataset.reindex.
What did you expect to happen?
Close to constant time with lazy reindexing.
Minimal Complete Verifiable Example
import numpy as np
import dask.array as da
import xarray as xr
ds = xr.Dataset(
data_vars={
"variable_name": ("time", da.from_array(
np.array(["test"], dtype=str), chunks=(1,)
))
},
coords={"time": ("time", np.array([0]))}
)
%timeit ds.reindex(time=np.linspace(0, 10, 50), method="nearest")
# 8.72 ms ± 148 μs per loop (mean ± std. dev. of 7 runs, 100 loops each)
%timeit ds.reindex(time=np.linspace(0, 10, 100), method="nearest")
# 16.3 ms ± 424 μs per loop (mean ± std. dev. of 7 runs, 100 loops each)
%timeit ds.reindex(time=np.linspace(0, 10, 1000), method="nearest")
# 152 ms ± 1.6 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)MVCE confirmation
- Minimal example — the example is as focused as reasonably possible to demonstrate the underlying issue in xarray.
- Complete example — the example is self-contained, including all data and the text of any traceback.
- Verifiable example — the example copy & pastes into an IPython prompt or Binder notebook, returning the result.
- New issue — a search of GitHub Issues suggests this is not a duplicate.
- Recent environment — the issue occurs with the latest version of xarray and its dependencies.
Relevant log output
Anything else we need to know?
This case shows up for example when using ds.interp with string variables.
Environment
INSTALLED VERSIONS
commit: None
python: 3.12.4 | packaged by conda-forge | (main, Jun 17 2024, 10:04:44) [MSC v.1940 64 bit (AMD64)]
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 58 Stepping 9, GenuineIntel
byteorder: little
LC_ALL: None
LANG: en
LOCALE: ('Swedish_Sweden', '1252')
libhdf5: 1.14.3
libnetcdf: 4.9.2
xarray: 2024.7.1.dev363+g99426cbb.d20240904
pandas: 2.2.2
numpy: 2.2.1
scipy: 1.14.1
netCDF4: 1.7.1
pydap: 3.5
h5netcdf: 1.3.0
h5py: 3.11.0
zarr: 2.18.2
cftime: 1.6.4
nc_time_axis: 1.4.1
iris: 3.9.0
bottleneck: 1.4.0
dask: 2024.11.2
distributed: 2024.11.2
matplotlib: 3.9.2
cartopy: 0.23.0
seaborn: 0.13.2
numbagg: None
fsspec: 2024.6.1
cupy: None
pint: None
sparse: None
flox: 0.9.10
numpy_groupies: 0.11.2
setuptools: 73.0.1
pip: 24.2
conda: None
pytest: 8.3.2
mypy: 1.14.1
IPython: 8.27.0
sphinx: 8.0.2